Tomas Folke

Columbia University | CU · Department of Health Policy and Management

Recent arguments claim that behavioral science has focused - to its detriment - on the individual over the system when construing behavioral interventions. In this commentary, we argue that tackling economic inequality using both framings in tandem is invaluable. By studying individuals who have overcome inequality, "positive deviants," and the sys...

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human–machine teaming expressed as communication with MToM c...

In order to be useful, XAI explanations have to be faithful to the AI system they seek to elucidate and also interpretable to the people that engage with them. There exist multiple algorithmic methods for assessing faithfulness, but this is not so for interpretability, which is typically only assessed through expensive user studies. Here we propose...

This project will use secondary data analysis to explore financial behaviors and economic inequality globally. We will investigate the patterns and predictors of positive deviance across and within 60 countries. Using this framework, we aim to better understand what incremental behaviors or individual factors might form the basis of more effective...

The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be done empirically, on a case-by-case basis, which prevents systematic theory-building in XA...

Behavioral science is increasingly used in public policy to understand and address various manifestations of inequalities. Yet evidence from effective population-level interventions is limited. One framework, known as positive deviance, emphasizes individuals from disadvantaged circumstances who have significantly better outcomes than are typical f...

No one likes to be wrong. Previous research has shown that participants may underweight information incompatible with previous choices, a phenomenon called confirmation bias. In this paper we argue that a similar bias exists in the way information is actively sought. We investigate how choice influences information gathering using a perceptual choi...

Due to the prevalence and importance of choices with uncertain outcomes, it is essential to establish what interventions improve risky decision-making, how they work, and for whom. Two types of low-intensity behavioural interventions are promising candidates: nudges and boosts. Nudges guide people to better decisions by altering how a choice is pre...

Pervading global narratives suggest that political polarization is increasing, yet the accuracy of such group meta-perceptions has been drawn into question. A recent US study suggests that these beliefs are inaccurate and drive polarized beliefs about out-groups. However, it also found that informing people of inaccuracies reduces those negative be...

Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain AI have been proposed to answer this challenge, but a lack of theory impedes the development of systematic...

To understand human decision-making, policymakers have traditionally turned to classical economic theory. However, human behavior often deviates from the expectation of rationality put forward by these classical approaches. One area where these deviations are clearly observable is in economic and consumer choices. Mapping out these examples of cons...

No one likes to be wrong. Previous research has shown that participants may underweight information incompatible with previous choices, a phenomenon called confirmation bias. In this paper we argue that a similar bias exists in the way information is actively sought. We investigate how choice influences information gathering using a perceptual choi...

Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends on humans understanding the AI well enough to predict when it is likely to make a mistake. In previous work we have found that humans tend to assume that the AI's decision...

Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts' trust in the AI agent...

Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain AI have been proposed to answer this challenge, but a lack of theory impedes the development of systematic...

Abstract Lebanon is rapidly adapting public services to meet local needs as well as those of refugees from conflict regions such as Syria. However, these challenges are complicated by high volumes of individuals with poor mental health, who are also at risk of poor decision-making and may avoid the use of health services due to low trust in governm...

State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts for how people typically reason about unfamiliar agents. We propose explicitly modelling the human explainee via Bayesian teaching, which evaluates explanations by how m...

Confirmation bias-the tendency to overweight information that matches prior beliefs or choices-has been shown to manifest even in simple reinforcement learning. In line with recent work, we find that participants learned significantly more from choice-confirming outcomes in a reward-learning task. What is less clear is whether asymmetric learning r...

Confirmation bias - the tendency to overweight information that matches prior beliefs or choices - has been shown to manifest even in simple reinforcement learning. In line with recent work, we find that participants learned significantly more from choice-confirming outcomes in a reward-learning task. What is less clear is whether asymmetric learni...

Lebanon is rapidly adapting public services meet local needs as well those of refugees from conflict regions such as Syria. However, these challenges are complicated by high volumes of individuals with poor mental health, who are also at risk of poor decision-making and may avoid use of health services due to low trust in government institutions, a...

A pervading global narrative suggests that political polarisation is increasing in the US and around the world. Beliefs in increased polarisation impact individual and group behaviours regardless of whether they are accurate or not. One driver of polarisation are beliefs about how members of the out-group perceive us, known as group meta-perception...

Behavioral policies are increasingly popular in a number of health care contexts. However, evidence of their effectiveness, specifically in low-income and highly disadvantaged populations, is limited. Some positive effects have been found for adaptive interventions, which merge more personalized approaches with advances in data collection and moder...

Background: An increasing number of international organisations and national governments have committed to well-being promotion. Unfortunately, important questions regarding how to assess well-being are still unresolved, making policy implementation and evaluation difficult. Methods: This research expanded on Huppert and So's (Soc Indic Res. 110...

Prospect theory is among the most influential frameworks in behavioural science, specifically in research on decision-making under risk. Kahneman and Tversky’s 1979 study tested financial choices under risk, concluding that such judgements deviate significantly from the assumptions of expected utility theory, which had remarkable impacts on science...

Background: Missed healthcare appointments (no-shows) are costly and operationally inefficient for health systems. No-show rates are particularly high for vulnerable populations, even though these populations often require additional care. Few studies on no-show behavior or potential interventions exist specifically for Federally Qualified Health...

Young adults increasingly require good financial literacy to make the most of the opportunities provided to them. Unfortunately, existing financial literacy measures that may assist with targeting interventions show low reliability, ceiling effects, and a high level of abstraction. To address this, we designed and assessed the psychometric properti...

https://osf.io/a96pt/ Young adults increasingly require good financial literacy to make the most of the opportunities provided to them. Unfortunately, existing financial literacy measures that may assist with targeting interventions show low reliability, ceiling effects, and a high level of abstraction. To address this, we designed and assessed th...

Behavioural interventions that directly influence decision-making are increasingly popular policy tools. Two prominent interventions used are nudges, which promote an optimal choice without restricting options, and boosts, which promote individual capabilities to make more informed choices. Direct comparison is a critical step toward understanding...

Kahneman and Tversky’s 1979 article proposing Prospect Theory is one of the most influential papers across all of the behavioural sciences. Their manuscript tested a series of binary financial choices with risk, ultimately concluding that behaviours deviate significantly from those presumed by prevailing theory at the time. In the forty years since...

https://www.routledge.com/Behavioral-Insights-for-Public-Policy-Concepts-and-Cases/Ruggeri/p/book/9781138484238 The first decades of the 21st century have offered a remarkable shift in how policies are made as well as who designs them. Until this period, advisory boards for local, regional, and national policy strategies largely comprised economis...

This chapter shows how insights from behavioral sciences can be used in public policy, specifically to improve decisions in the area of financial choice.

Humans can often report a subjective sense of confidence in a decision before knowing its outcome. Such confidence judgements are positively correlated to accuracy in perceptual and memory tasks, but the strength of this relationship (known as metacognitive accuracy) differs across people and contexts. Computationally, confidence judgements are bel...

Humans can reflect on decisions and report variable levels of confidence. But why maintain an explicit representation of confidence for choices that have already been made and therefore cannot be undone? Here we show that an explicit representation of confidence is harnessed for subsequent changes of mind. Specifically, when confidence is low, part...

The first annual report of the Policy Research Group in the Department of Psychology at the University of Cambridge. This report is written with support from the Junior Researcher Programme.

Recent research indicating that bilingualism is associated with enhanced executive function suggests that this enhancement may operate within a broader spectrum of cognitive abilities than previously thought (e.g., Stocco & Prat, 2014). In this study, we focus on metacognition or the ability to evaluate one’s own cognitive performance (Flavell, 197...